Traditional approaches on board games have failed on larger boards, given large state spaces, complex optimal value functions making it infeasible for the to learn using self-play, and underdeveloped algorithms, that have slower/skewed learning curves. The problem starts to become even more complex as soon as multi-agent strategies come into play. Cooperative v/s independent multi agent strategies have their own advantages and disadvantages [1]}. Cooperative agents have shown improvements in scores at the cost of speed. Independent agents, on the other hand, have shown the reverse to be true. While the idea of having both agents have access to the other’s state and action space is an acceptable workaround, such a setting is not possible to have in a few cases, like card games and certain kind of board games.
